Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Med., 05 August 2025

Sec. Family Medicine and Primary Care

Volume 12 - 2025 | https://doi.org/10.3389/fmed.2025.1588835

This article is part of the Research TopicMechanisms and Management of Inflammation-driven Cardiovascular Risk: from Obesity and Diabetes to Autoimmunity and CancerView all 12 articles

Associations between Life's Essential 8 scores and systemic immune-inflammation index among American-adult populations without cardiovascular disease

  • 1Department of Critical Care Medicine, Ningbo Medical Center Lihuili Hospital, Ningbo, China
  • 2Department of Otolaryngology, Jiangshan People's Hospital, Quzhou, China
  • 3Zhejiang Key Laboratory of Geriatrics and Geriatrics Institute of Zhejiang Province, Zhejiang Hospital, Hangzhou, China

Background: Cardiovascular health (CVH) profoundly impacts human health and quality of life. Increasing evidence suggests a close association between cardiovascular disease (CVD) and systemic immune-inflammatory levels. This study explores the potential correlation between Life's Essential 8 (LE8) scores and the systemic immune-inflammation index (SII), a novel immune-inflammatory index among US adults. This study provides evidence supporting the role of systemic inflammation reduction in promoting CVH.

Methods: Utilizing data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007–2018, we investigated information from 21,403 adult participants. Participants were categorized into low CVH (0–49), moderate CVH (50–79), and high CVH (80–100) groups based on LE8 scores. We employed weighted linear regression analysis and subgroup analysis, along with restricted cubic spline curves (RCS) to explore the association between LE8 and SII and the dose-response relationship.

Result: A significant negative correlation was found between higher LE8 scores and lower SII levels. Compared to the low CVH group, the β coefficients for SII in the CVH moderate and CVH high groups were −40.02 (95% CI: −58.99 to −21.05, p < 0.001) and −77.62 (95% CI: −102.4 to −52.80, p < 0.001), respectively. Additionally, both LE8 scores and health behaviors scores showed a significant linear negative correlation with SII. There was an inverted “U-shaped” non-linear relationship between health factors scores and SII, and the health factor score was 284.724, with a maximum SII threshold of 518.010 (1,000 cells/μl). The health factor score is positively associated with SII below 518.010 and negatively associated above this threshold. Subgroup analyses showed that the negative association was stable in most subgroups. The negative correlation was insignificant among those aged >65 and Mexican Americans.

Conclusion: LE8 showed a significant negative correlation with SII. The findings suggest that maintaining higher LE8 scores to some extent promotes CVH and helps alleviate systemic inflammation, potentially benefiting overall health.

1 Introduction

To improve cardiovascular health (CVH) in populations, the American Heart Association (AHA) introduced a new concept of CVH in 2010, along with measures for assessing and monitoring it, known as Life's Simple 7 (LS7) (1). LS7 is based on seven health behaviors and factors, including diet, physical activity (PA), smoking, blood glucose, blood lipids, blood pressure (BP), and body mass index (BMI) (2). With increasing research, poor sleep health was identified as being associated with an increased risk of cardiovascular mortality (36). Studies suggest that sleep affects BP, inflammation, glucose homeostasis, and other factors related to CVH (79), highlighting the potential importance of sleep for overall health and cardiometabolic health. Therefore, in 2022, AHA introduced a new CVH score, Life's Essential 8 (LE8), consisting of four main health behaviors and health factors. The four health behaviors include diet, PA, smoking state, and sleep and the four health factors include BMI, BP, blood lipids, and blood glucose (10). Both LS7 and LE8 aim to promote individual and population CVH. It has been suggested that when CVH is optimized, the aforementioned behaviors and factors are associated with longer cardiovascular disease (CVD)-free survival, overall life expectancy, and higher quality of life (1113).

The development of CVD is closely related to inflammation and the immune system. Neutrophils are effectors of the innate immune response and regulate processes such as autoimmunity and chronic inflammation (14). Platelets maintain homeostasis, are involved in mediating acute and chronic inflammatory processes, and contribute to the inflammatory environment (15). Lymphocytes are the key cells of the adaptive immune response that link the innate and adaptive immune responses (16). A novel immune-inflammatory index, the systemic immune-inflammation index (SII), was first proposed by Hu et al. (17) and defined as “platelet * neutrophil count/lymphocyte count.” It reflects the balance between inflammation and immune response, with elevations suggesting an increased inflammatory state of the disease and a weakened immune response. Previous studies have shown that elevated SII is strongly associated with CVD. For instance, research has demonstrated a significant association between higher SII and increased risk of CVD, as well as its prognosis and mortality rates (1820). SII has been identified as a risk factor for atrial fibrillation (21), systolic insufficiency in cardiomyopathies (22), and may serve as an independent predictor for massive pulmonary embolism (23). In addition, studies have found that SII is an independent risk factor for the development of coronary heart disease (CHD) in patients with non-alcoholic fatty liver disease (NAFLD) and is closely associated with the prediction and severity of CHD (24). Furthermore, SII has a certain predictive value for the increased risk of all-cause and cardiovascular mortality in adults with hypertension (25).

This study utilized a nationally representative cohort with diverse racial backgrounds, employing the complex multi-stage probability sampling design of the National Health and Nutrition Examination Survey (NHANES) database. Explore the relationship between LE8 scores and SII. Elaborate on whether maintaining optimal CVH status can improve systemic inflammation and potentially reduce the occurrence of various other diseases.

2 Methods

2.1 Data source and study participants

All data for this study were obtained from the NHANES in the US. Information regarding the study design and data from the NHANES database can be publicly accessed at https://www.cdc.gov/nchs/nhanes/. Briefly, the NHANES database comprises five main categories of data, including demographic data, dietary data, examination data, laboratory data, questionnaire data, and restricted access data. It employs a complex, multi-stage, probability sampling method, providing extensive information on the nutrition and health of the general U.S. population, with all survey participants consenting to participation. The NHANES study has obtained approval from the National Center for Health Statistics (NCHS) Research Ethics Review Board, and detailed information regarding the approval of the NCHS Research Ethics Review Board can be accessed on the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/default.aspx).

This study included data from NHANES from 2007 to 2018, totaling six consecutive cycles with a total of 59,842 participants. Adult participants with complete data on LE8 scores, SII, etc. were mainly included in this study. Figure 1 shows the screening flowchart of this study. After applying the exclusion criteria, the following participants were excluded from this study: (1) 25,072 participants younger than 20 years of age; (2) 3,066 participants with missing SII data; (3) 7,143 participants who lacked complete CVH data; and (4) 3,158 individuals with self-reported coronary heart disease, angina pectoris, heart attack, and stroke were excluded. A final total of 21,403 participants were included.

Figure 1
Flowchart illustrating participant selection from NHANES records (2007-2018) starting with 59,842 people. Participants under 20, without SII data, and incomplete CVH metrics were excluded, leaving 21,403 final participants with complete data, excluding those with coronary diseases, angina pectoris, heart attack, and stroke.

Figure 1. Flowchart of participants. SII, systemic immune-inflammation index; CVH, cardiovascular health.

2.2 Measurement of LE8 and SII

LE8 comprises eight indicators, divided into health behaviors (diet, PA, smoking state, and sleep) and four health factors (BMI, BP, blood lipids, and blood glucose). These eight items are scored according to publicly available official calculation methods. Each item is scored on a scale of 0–100 points, and the total score is derived from the unweighted average of the eight indicators. Based on the final LE8 score, CVH is categorized into three groups: low (0–49), moderate (50–79), and high (80–100) (11). Among them, the dietary indicator is evaluated based on the Healthy Eating Index-2015 (HEI-2015) and includes only participants with 2-day dietary data for this study (26, 27). Additionally, BMI and BP are obtained from physical examination data. PA, smoking state, and sleep are collected through self-reported questionnaires. Blood lipids and blood glucose were obtained from laboratory data. Detailed algorithms for calculating LE8 scores for various indicators in NHANES data can be found in previous literature (28).

The platelet count, neutrophil count, and lymphocyte count used to calculate the SII are obtained from laboratory data of whole blood cell counts. Whole blood cell counts are measured by the NHANES Mobile Examination Center (MEC) using the Beckman Coulter DxH 800 instrument and are expressed in units of ( × 1,000 cells/μl). The specific formula for calculating the SII follows previous literature and is defined as platelet count × neutrophil count/lymphocyte count (17).

2.3 Covariates

Based on previous research experience (29, 30), potential relevant covariates included in the analysis are age, poverty impact ratio (PIR), gender (male, female), race/ethnicity (Mexican American, Non-Hispanic Black, Non-Hispanic White, Other Hispanic, and Other/multiracial), white blood cell count (WBC), neutrophil count, platelet count, lymphocyte count, blood potassium, blood sodium, creatinine, albumin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), urea nitrogen, total calcium, lactate dehydrogenase (LDH), phosphorus, total bilirubin, and uric acid.

2.4 Statistical method

According to the NHANES database analysis guidelines, weighted methods were employed to reduce data variability for statistical analysis (weight parameter: WTDR2D/6). The normality of the independent and dependent variables was first verified using the adoption of the Lilliefors test; the results showed that both the independent and dependent variables were non-normally distributed. Continuous variables were represented using weighted means and standard deviations, and group comparisons were made using the Kruskal–Wallis H test. Categorical variables were presented as unweighted counts and weighted percentages, with group comparisons conducted using the chi-square test. Three models were utilized: Model 1 (unadjusted), Model 2 (adjusted for age, gender, PIR, and race), and Model 3 (adjusted for age, gender, PIR, race, creatinine, albumin, ALT, AST, urea nitrogen, total calcium, LDH, phosphorus, total bilirubin, and uric acid). The dependent variable was SII, and weighted linear regression analyses were performed to assess the strength of the variables' associations with LE8, its components, and CVH three groups (low, moderate, and high), calculating β coefficients. Potential non-linear relationships between SII and LE 8 scores, health behaviors, and health factors were explored by comparing model fit metrics at different nodes after using RCS (three nodes) and using likelihood ratio tests. Subsequently, subgroup analyses were performed based on age, gender, race, and PIR, visually represented using forest plots. All data analyses were conducted using SPSS 26.0 and R 4.2.3, with statistical significance set at p < 0.05.

3 Results

3.1 Baseline characteristics of the study population

Table 1 shows the baseline characteristics of this study population, comprising a total of 21,403 participants. Based on LE8 scores, individuals were categorized into low CVH (0–49), moderate CVH (50–79), and high CVH (80–100) groups. It was observed that the high CVH group consisted predominantly of females and mostly non-Hispanic White individuals. Additionally, they exhibited younger age and higher income levels. As expected, individuals with higher CVH scores showed lower levels of SII, along with lower neutrophil count, platelet count, white blood cell count, lymphocyte count, creatinine, AST, ALT, and LDH. Conversely, total bilirubin and blood phosphorus levels were higher in this group.

Table 1
www.frontiersin.org

Table 1. Weighted characteristics of the participants by CVH groups.

3.2 Association between LE8 Score and SII

We established three models: Model 1 (unadjusted), Model 2 (adjusted for age, gender, PIR, and race), and Model 3 (adjusted for age, gender, PIR, race, creatinine, albumin, ALT, AST, urea nitrogen, total calcium, LDH, phosphorus, total bilirubin, and uric acid), and conducted weighted linear regression analysis and RCS. As shown in Table 2, the LE8 score was analyzed as a continuous variable. The results revealed a significant negative correlation between SII levels and LE8 scores [β95% CI: −2.379 (−2.843 to −1.916), p < 0.001]. The β coefficient for LE8 scores was −2.379 (1,000 cells/μl/point), indicating that an increase of one unit in SII was associated with a decrease of 2.379 points in LE8 scores. This negative correlation remained significant after controlling for relevant covariates [Model 2 and Model 3; β95% CI: −2.657 (−3.087 to −2.227), p < 0.001; β95% CI: −2.029 (−2.539 to −1.520), p < 0.001].

Table 2
www.frontiersin.org

Table 2. Weighted linear regression analysis of SII and LE8 scores, LE8 component scores, and LE8 scores groups.

Further analysis of the individual components of LE8 scores revealed that SII was significantly negatively correlated with PA, smoke, BP, diet, BMI, and glucose scores (p < 0.001). Among these, BP scores contributed the most, while there was no significant correlation with sleep or non-high-density-lipoprotein cholesterol (non-LDL) scores (p > 0.05). Additionally, when stratified by LE8 scores and compared with the low CVH group, the β coefficients of SII in the moderate CVH group were −53.02 (95% CI: −72.51 to −33.54, p < 0.001), and in the high CVH group were −108.60 (95% CI: −132.2 to −84.97, p < 0.001). This indicates that SII levels were significantly lower in the moderate CVH group and high CVH group than in the low CVH group, and were lowest in the high CVH group. This correlation remained significant after controlling for relevant covariates (p < 0.001). These findings suggest that higher LE8 scores are associated with lower SII levels, demonstrating a negative correlation.

To explore the potential non-linear relationship and dose-response relationship between SII and LE8 scores, we conducted RCS analysis in Model 3, examining the association of SII with LE8 scores, health factor scores, and health behavior scores. As shown in Figure 2, the p-values for non-linearity between SII and LE8 scores (Figure 2A) and health behavior scores (Figure 2C) were both >0.05, indicating no non-linear relationship between SII and LE8 scores or health behaviors scores. However, interestingly, there was a non-linear relationship between SII and health factors scores (Figure 2B, p < 0.05), displaying a reverse “U” shape, and the health factor score was 284.724, with a maximum SII threshold of 518.010 (1,000 cells/μl). The health factor score is positively associated with SII below 518.010 and negatively associated above this threshold.

Figure 2
Three line graphs labeled A, B, and C show the relationship between different scores and SII levels (5mol/L). Graph A compares LE8 Score with SII, showing a downward trend with a p-overall of 0.001. Graph B compares Health Factors Scores with SII, showing a curve with a similar p-overall significance but a more notable non-linear aspect. Graph C compares Health Behaviors Scores with SII, showing a linear decline with p-overall < 0.001. Shaded areas indicate confidence intervals.

Figure 2. Dose-response relationship curves between SII and adjusted β values for LE8 scores, health factors scores, and health behaviors scores. (A) LE8 scores, (B) health factors scores, (C) health behaviors scores. The model was adjusted for age, gender, PIR, race, creatinine, albumin, ALT, AST, urea nitrogen, total calcium, LDH, phosphorus, total bilirubin, and uric acid. SII, systemic immune-inflammation index; LE8, Life's Essential 8; PIR, poverty impact ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; LDH, lactate dehydrogenase.

3.3 Subgroup analysis

To assess the stability of this negative association, we performed subgroup analyses for age, gender, race, and PIR. In Figure 3, stratification by gender and PIR revealed significant correlations between SII and LE8 scores in all subgroups. After stratifying by age, significant correlations were observed only among participants aged < 65 years, but not in those aged >65 years. When stratified by race significant associations were observed in participants of all races except Mexican American.

Figure 3
Forest plot showing the association of different demographic factors with an outcome. Factors include age, gender, race, and poverty-to-income ratio (PIR). Horizontal lines represent confidence intervals for each category, centered on red squares indicating effect sizes. Age less than sixty-five and gender (female and male) show significant associations with p-values less than 0.001. Non-Hispanic Black, Non-Hispanic White, and Other Hispanic races also show significant associations with varying confidence intervals. PIR categories indicate significant negative associations with p-values less than 0.001.

Figure 3. Forest plot for subgroup analysis. The model was adjusted for age, gender, PIR, race, creatinine, albumin, ALT, AST, urea nitrogen, total calcium, LDH, phosphorus, total bilirubin, and uric acid. PIR, poverty impact ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; LDH, lactate dehydrogenase.

4 Discussion

In this study, we found a significant negative correlation between SII levels and LE8 scores. Even after controlling for relevant covariates, this negative correlation remained significant, indicating that higher SII levels were associated with lower LE8 scores. The LE8 scores encompass four health behaviors scores and four health factors scores. Interestingly, there was no non-linear relationship between SII levels and LE8 scores or health behavior scores. In contrast, a reverse “U-shaped” non-linear relationship existed with the four health factors scores, further suggesting that when SII > 518.010 (1,000 cells/μl), higher SII levels were associated with lower scores in health factors, potentially leading to a decline in CVH status. Linear regression analysis indicated that among these four health factors scores, BP made the greatest contribution. We further conducted subgroup analyses by age, gender, race, and PIR, and the results showed that this negative correlation remained stable in most strata. However, it was not significant in individuals aged >65 years old or those of Mexican American ethnicity, suggesting a cautious interpretation of these results in these populations. In conclusion, this study suggests that SII may be a readily accessible and valuable indicator for assessing CVH status in non-CVD populations.

CVD is a leading cause of global mortality and disability. According to reports from 2019, CVD accounted for over 18 million deaths worldwide, approximately one-third of all global deaths. High systolic BP, dietary risks, high and low-density lipoprotein cholesterol, air pollution, high body mass index, smoking, high blood sugar, and renal dysfunction are major risk factors for CVD (31). A higher CVH state is associated with lower CVD risk, with mechanisms involving inflammation, endothelial function, atherosclerosis, cardiac stress and remodeling, hemostatic factors, and epigenetics (13, 32, 33). Studies suggest that low-grade chronic inflammation increases the risk of atherosclerosis and insulin resistance, leading to persistent low-level immune system activity (34). Furthermore, patients with chronically immune-mediated inflammatory diseases have an increased risk of CVD (35). The immune system and inflammatory processes play a crucial role in the pathogenesis of atherosclerosis (36).

SII, as a novel indicator quantifying systemic immune and inflammatory responses, incorporates indices of platelets, neutrophils, and lymphocytes. Previous research has primarily focused on the relationship between SII and CVD. For instance, elevated SII has been associated with increased CVD risk and mortality rates (1820, 37, 38). Additionally, higher SII levels increase the risk of hemorrhagic and ischemic stroke subtypes as well as overall mortality (39). However, there has been limited research on the association between the LE8 scores and SII. Therefore, this study, after excluding relevant cardiovascular-related diseases, explored for the first time the relationship between LE8 scores and its components with SII among adults. The findings indicated a significant negative correlation between SII and both LE8 scores and its components, suggesting that maintaining optimal CVH status may improve systemic inflammation.

LE8, as a comprehensive indicator, considers not only health factors such as BP and lipids but also health behaviors like sleep, nicotine exposure, and exercise. Previous studies have shown that sleep-related disorders in adults, such as sleep duration, sleep problems, high risk of obstructive sleep apnea (OSA), and daytime sleepiness, are associated with higher levels of SII (40). Our study found a significant negative linear correlation between SII and health behavior scores, and although there was a negative association between SII and sleep scores, this association was not statistically significant. This may be related to differences in the methods used to assess sleep scores. Additionally, smoking status is believed to influence the relationship between SII and metabolic syndrome (41). In our study, smoking score showed a significant negative correlation with SII, suggesting that smoking not only affects CVH status but may also impact systemic inflammation. Therefore, our study suggests that in adults without CVD, interventions such as smoking cessation, improving diet, regular exercise, and controlling blood glucose and BP may be more effective in reducing inflammation in individuals with low LE8 scores. It's noteworthy that a non-linear relationship exists between SII and health factors scores, which may suggest a deeper connection between SII and overall CVH. Although this study couldn't pinpoint the exact relationship between SII and the four health factors, when SII = 518.010 (1,000 cells/μl), it may suggest that maintaining these four health factors at optimal levels could be more beneficial for maintaining CVH status.

Subgroup analysis in this study indicated that the association between SII and LE8 scores remained stable in most subgroups. However, in the subgroup analysis stratified by age, the relationship between SII and LE8 scores was not significant among individuals aged over 65 years. This suggests that, after adjusting for confounding factors, the elevated SII levels in individuals aged over 65 are not significantly associated with CVH status. Firstly, the declining immune system in the older adult leads to increased senescent cells, causing systemic inflammation (42). Additionally, with aging, there is a mild pro-inflammatory state termed inflammaging. On one hand, aging characteristics exhibit mild pro-inflammatory states referred to as aging-associated inflammatory responses (43, 44). On the other hand, senescent cells contribute to the production of pro-inflammatory cytokines, collectively known as the senescence-associated secretory phenotype (45). These mechanisms may weaken the correlation between SII and LE8 scores. In the subgroup analysis based on race, this relationship was not significant among Mexican Americans, which may be partly related to their environmental and genetic risk factors predisposing them to a high-risk status for CVD (46, 47).

Our study has several strengths. First, we are the first to evaluate the relationship between SII and LE8, utilizing dose-response analyses to more effectively illustrate the associations between SII, LE8, health behaviors, and health factors. Second, compared to other commonly used inflammatory markers, such as C-reactive protein (CRP) or interleukin-6, SII serves as a low-cost biomarker with promising clinical utility. Third, our study leveraged NHANES data, which employs a complex, multistage probability sampling design and accounts for relevant confounding factors, ensuring a more representative analysis. This enhances the applicability of our findings to broader populations.

Nevertheless, certain limitations should be acknowledged. To begin with, this study is a cross-sectional design based on a U.S. population, and the use of longitudinal analyses or causal modeling would be more conducive to revealing the deeper mechanisms underlying the study's finding of an association between LE8 and SII. This is particularly critical as systemic inflammation can both influence and be influenced by cardiovascular health, emphasizing the need for further investigation into the underlying mechanisms. Second, the measurements of key variables such as lifestyle habits in the study were only self-reported, which has questionable reliability and validity, and the quality of the study could be further improved by using more objective measures. Moreover, SII was measured or calculated at a single time point, whereas dynamic monitoring over time might provide more accurate insights in clinical settings. Finally, while this study adjusted for multiple covariates, unmeasured confounding factors, such as socioeconomic or genetic variables, may still have influenced the results.

5 Conclusion

This study demonstrates a negative linear correlation between SII and health behavior, health factors, and LE8 scores. Additionally, there exists a non-linear relationship between SII and health factors, displaying a reverse “U” shape. These findings suggest that maintaining optimal LE8 scores not only promotes CVH status but also helps alleviate systemic inflammation, thereby potentially benefiting overall health. However, the causal mechanisms underlying these associations require further investigation and elucidation.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Ethics statement

The portions of this study involving human data were conducted in accordance with the Declaration of Helsinki and the NCHS Research Ethics Review Committee reviewed and approved the NHANES study protocol. All survey participants signed informed consent forms. Due to the utilization of data sourced from publicly accessible databases, the Ethics Committee of LiHuili Hospital, Ningbo Medical Center, has granted an exemption from ethical review.

Author contributions

S-SH: Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. X-SY: Data curation, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. TL: Data curation, Investigation, Visualization, Writing – review & editing. Z-YX: Investigation, Visualization, Writing – review & editing. H-YM: Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing. Z-XY: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the Zhejiang Provincial Natural Science Foundation of China (LY21H150002).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Gen AI was used in the creation of this manuscript.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Abbreviations

CVH, cardiovascular health; AHA, American Heart Association; LS7, Life's Simple 7; PA, physical activity; BP, blood pressure; BMI, body mass index; LE8, Life's Essential 8; CVD, cardiovascular disease; SII, systemic immune-inflammation index; CHD, coronary heart disease; NAFLD, non-alcoholic fatty liver disease; NHANES, National Health and Nutrition Examination Survey; NCHS, National Center for Health Statistics; HEI-2015, Healthy Eating Index-2015; MEC, Mobile Examination Center; PIR, poverty impact ratio; LDL, low-density lipoprotein cholesterol; WBC, white blood cell; ALT, alanine aminotransferase; AST, aspartate aminotransferase; LDH, lactate dehydrogenase; RCS, restricted cubic spline; OSA. obstructive sleep apnea; CRP, C-reactive protein.

References

1. Ma H, Wang X, Xue Q, Li X, Liang Z, Heianza Y, et al. Cardiovascular health and life expectancy among adults in the United States. Circulation. (2023) 147:1137–46. doi: 10.1161/CIRCULATIONAHA.122.062457

PubMed Abstract | Crossref Full Text | Google Scholar

2. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation. (2010) 121:586–613. doi: 10.1161/CIRCULATIONAHA.109.192703

PubMed Abstract | Crossref Full Text | Google Scholar

3. Lao XQ, Liu X, Deng H-B, Chan T-C, Ho KF, Wang F, et al. Sleep quality, sleep duration, and the risk of coronary heart disease: a Prospective Cohort Study With 60,586 adults. J Clin Sleep Med. (2018) 14:109–17. doi: 10.5664/jcsm.6894

PubMed Abstract | Crossref Full Text | Google Scholar

4. Nagai M, Hoshide S, Kario K. Sleep duration as a risk factor for cardiovascular disease- a review of the recent literature. Curr Cardiol Rev. (2010) 6:54–61. doi: 10.2174/157340310790231635

PubMed Abstract | Crossref Full Text | Google Scholar

5. Yang X, Chen H, Li S, Pan L, Jia C. Association of sleep duration with the morbidity and mortality of coronary artery disease: a meta-analysis of prospective studies. Heart Lung Circ. (2015) 24:1180–90. doi: 10.1016/j.hlc.2015.08.005

PubMed Abstract | Crossref Full Text | Google Scholar

6. Ayas NT, White DP, Manson JE, Stampfer MJ, Speizer FE, Malhotra A, et al. A prospective study of sleep duration and coronary heart disease in women. Arch Intern Med. (2003) 163:205–9. doi: 10.1001/archinte.163.2.205

PubMed Abstract | Crossref Full Text | Google Scholar

7. Kanki M, Nath AP, Xiang R, Yiallourou S, Fuller PJ, Cole TJ, et al. Poor sleep and shift work associate with increased blood pressure and inflammation in UK Biobank participants. Nat Commun. (2023) 14:7096. doi: 10.1038/s41467-023-42758-6

PubMed Abstract | Crossref Full Text | Google Scholar

8. Alvarez GG, Ayas NT. The impact of daily sleep duration on health: a review of the literature. Prog Cardiovasc Nurs. (2004) 19:56–9. doi: 10.1111/j.0889-7204.2004.02422.x

PubMed Abstract | Crossref Full Text | Google Scholar

9. Hale L, Troxel W, Buysse DJ. Sleep health: an opportunity for public health to address health equity. Annu Rev Public Health. (2020) 41:81–99. doi: 10.1146/annurev-publhealth-040119-094412

PubMed Abstract | Crossref Full Text | Google Scholar

10. Liu Y, Wheaton AG, Chapman DP, Cunningham TJ, Lu H, Croft JB. Prevalence of healthy sleep duration among adults–United States, 2014. MMWR Morb Mortal Wkly Rep. (2016) 65:137–41. doi: 10.15585/mmwr.mm6506a1

PubMed Abstract | Crossref Full Text | Google Scholar

11. Lloyd-Jones DM, Allen NB, Anderson CAM, Black T, Brewer LC, Foraker RE, et al. Life's Essential 8: updating and enhancing the American Heart Association's construct of cardiovascular health: a presidential advisory from the American Heart Association. Circulation. (2022) 146:e18–43. doi: 10.1161/CIR.0000000000001078

PubMed Abstract | Crossref Full Text | Google Scholar

12. Perak AM, Ning H, Khan SS, Bundy JD, Allen NB, Lewis CE, et al. Associations of late adolescent or young adult cardiovascular health with premature cardiovascular disease and mortality. J Am Coll Cardiol. (2020) 76:2695–707. doi: 10.1016/j.jacc.2020.10.002

PubMed Abstract | Crossref Full Text | Google Scholar

13. Fang N, Jiang M, Fan Y. Ideal cardiovascular health metrics and risk of cardiovascular disease or mortality: a meta-analysis. Int J Cardiol. (2016) 214:279–83. doi: 10.1016/j.ijcard.2016.03.210

PubMed Abstract | Crossref Full Text | Google Scholar

14. Liew PX, Kubes P. The neutrophil's role during health and disease. Physiol Rev. (2019) 99:1223–48. doi: 10.1152/physrev.00012.2018

PubMed Abstract | Crossref Full Text | Google Scholar

15. Koupenova M, Clancy L, Corkrey HA, Freedman JE. Circulating platelets as mediators of immunity, inflammation and thrombosis. Circ Res. (2018) 122:337–51. doi: 10.1161/CIRCRESAHA.117.310795

PubMed Abstract | Crossref Full Text | Google Scholar

16. Gray KJ, Gibbs JE. Adaptive immunity, chronic inflammation and the clock. Semin Immunopathol. (2022) 44:209–24. doi: 10.1007/s00281-022-00919-7

PubMed Abstract | Crossref Full Text | Google Scholar

17. Hu B, Yang X-R, Xu Y, Sun Y-F, Sun C, Guo W, et al. Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma. Clin Cancer Res. (2014) 20:6212–22. doi: 10.1158/1078-0432.CCR-14-0442

PubMed Abstract | Crossref Full Text | Google Scholar

18. Zhou Y-X, Li W-C, Xia S-H, Xiang T, Tang C, Luo J-L, et al. Predictive value of the systemic immune inflammation index for adverse outcomes in patients with acute ischemic stroke. Front Neurol. (2022) 13:836595. doi: 10.3389/fneur.2022.836595

PubMed Abstract | Crossref Full Text | Google Scholar

19. Xia Y, Xia C, Wu L, Li Z, Li H, Zhang J. Systemic immune inflammation index (SII), system inflammation response index (SIRI) and risk of all-cause mortality and cardiovascular mortality: a 20-year follow-up cohort study of 42,875 US Adults. J Clin Med. (2023) 12:1128. doi: 10.3390/jcm12031128

PubMed Abstract | Crossref Full Text | Google Scholar

20. Ye Z, Hu T, Wang J, Xiao R, Liao X, Liu M, et al. Systemic immune-inflammation index as a potential biomarker of cardiovascular diseases: a systematic review and meta-analysis. Front Cardiovasc Med. (2022) 9:933913. doi: 10.3389/fcvm.2022.933913

PubMed Abstract | Crossref Full Text | Google Scholar

21. Selcuk M, Cinar T, Saylik F, Dogan S, Selcuk I, Orhan AL. Predictive value of systemic immune inflammation index for postoperative atrial fibrillation in patients undergoing isolated coronary artery bypass grafting. Medeni Med J. (2021) 36:318–24. doi: 10.4274/MMJ.galenos.2021.37998

PubMed Abstract | Crossref Full Text | Google Scholar

22. Zhang Y, Liu W, Yu H, Chen Z, Zhang C, Ti Y, et al. Value of the systemic immune-inflammatory index (SII) in predicting the prognosis of patients with peripartum cardiomyopathy. Front Cardiovasc Med. (2022) 9:811079. doi: 10.3389/fcvm.2022.811079

PubMed Abstract | Crossref Full Text | Google Scholar

23. Gok M, Kurtul A. A novel marker for predicting severity of acute pulmonary embolism: systemic immune-inflammation index. Scand Cardiovasc J. (2021) 55:91–6. doi: 10.1080/14017431.2020.1846774

PubMed Abstract | Crossref Full Text | Google Scholar

24. Dong W, Gong Y, Zhao J, Wang Y, Li B, Yang Y, et al. A combined analysis of TyG index, SII index, and SIRI index: positive association with CHD risk and coronary atherosclerosis severity in patients with NAFLD. Front Endocrinol. (2023) 14:1281839. doi: 10.3389/fendo.2023.1281839

PubMed Abstract | Crossref Full Text | Google Scholar

25. Cao Y, Li P, Zhang Y, Qiu M, Li J, Ma S, et al. Association of systemic immune inflammatory index with all-cause and cause-specific mortality in hypertensive individuals: results from NHANES. Front Immunol. (2023) 14:1087345. doi: 10.3389/fimmu.2023.1087345

PubMed Abstract | Crossref Full Text | Google Scholar

26. Kennedy ET, Ohls J, Carlson S, Fleming K. The healthy eating index: design and applications. J Am Diet Assoc. (1995) 95:1103–8. doi: 10.1016/S0002-8223(95)00300-2

PubMed Abstract | Crossref Full Text | Google Scholar

27. Krebs-Smith SM, Pannucci TE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, et al. Update of the healthy eating index: HEI-2015. J Acad Nutr Diet. (2018) 118:1591–602. doi: 10.1016/j.jand.2018.05.021

PubMed Abstract | Crossref Full Text | Google Scholar

28. Lloyd-Jones DM, Ning H, Labarthe D, Brewer L, Sharma G, Rosamond W, et al. Status of cardiovascular health in US adults and children using the American Heart Association's New “Life's Essential 8” metrics: prevalence estimates from the National Health and Nutrition Examination Survey (NHANES), 2013 through 2018. Circulation. (2022) 146:822–35. doi: 10.1161/CIRCULATIONAHA.122.060911

PubMed Abstract | Crossref Full Text | Google Scholar

29. Liu W, Wang J, Wang M, Hou H, Ding X, Ma L, et al. Oxidative stress factors mediate the association between life's essential 8 and accelerated phenotypic aging: NHANES 2005-2018. J Gerontol A Biol Sci Med Sci. (2024) 79:glad240. doi: 10.1093/gerona/glad240

PubMed Abstract | Crossref Full Text | Google Scholar

30. Wang L, Yi J, Guo X, Ren X. Associations between life's essential 8 and non-alcoholic fatty liver disease among US adults. J Transl Med. (2022) 20:616. doi: 10.1186/s12967-022-03839-0

PubMed Abstract | Crossref Full Text | Google Scholar

31. Roth GA, Mensah GA, Fuster V. The global burden of cardiovascular diseases and risks: a compass for global action. J Am Coll Cardiol. (2020) 76:2980–1. doi: 10.1016/j.jacc.2020.11.021

PubMed Abstract | Crossref Full Text | Google Scholar

32. Joyce BT, Gao T, Zheng Y, Ma J, Hwang S-J, Liu L, et al. Epigenetic age acceleration reflects long-term cardiovascular health. Circ Res. (2021) 129:770–81. doi: 10.1161/CIRCRESAHA.121.318965

PubMed Abstract | Crossref Full Text | Google Scholar

33. Pottinger TD, Khan SS, Zheng Y, Zhang W, Tindle HA, Allison M, et al. Association of cardiovascular health and epigenetic age acceleration. Clin Epigenetics. (2021) 13:42. doi: 10.1186/s13148-021-01028-2

PubMed Abstract | Crossref Full Text | Google Scholar

34. Soysal P, Arik F, Smith L, Jackson SE, Isik AT. Inflammation, frailty and cardiovascular disease. Adv Exp Med Biol. (2020) 1216:55–64. doi: 10.1007/978-3-030-33330-0_7

PubMed Abstract | Crossref Full Text | Google Scholar

35. Baena-Díez JM, Garcia-Gil M, Comas-Cufí M, Ramos R, Prieto-Alhambra D, Salvador-González B, et al. Association between chronic immune-mediated inflammatory diseases and cardiovascular risk. Heart. (2018) 104:119–26. doi: 10.1136/heartjnl-2017-311279

PubMed Abstract | Crossref Full Text | Google Scholar

36. Chistiakov DA, Kashirskikh DA, Khotina VA, Grechko AV, Orekhov AN. Immune-inflammatory responses in atherosclerosis: the role of myeloid cells. J Clin Med. (2019) 8:1798. doi: 10.3390/jcm8111798

PubMed Abstract | Crossref Full Text | Google Scholar

37. Huang J, Zhang Q, Wang R, Ji H, Chen Y, Quan X, et al. Systemic immune-inflammatory index predicts clinical outcomes for elderly patients with acute myocardial infarction receiving percutaneous coronary intervention. Med Sci Monit. (2019) 25:9690–701. doi: 10.12659/MSM.919802

PubMed Abstract | Crossref Full Text | Google Scholar

38. Esenboga K, Kurtul A, Yamantürk YY, Tan TS, Tutar DE. Systemic immune-inflammation index predicts no-reflow phenomenon after primary percutaneous coronary intervention. Acta Cardiol. (2022) 77:59–65. doi: 10.1080/00015385.2021.1884786

PubMed Abstract | Crossref Full Text | Google Scholar

39. Jin Z, Wu Q, Chen S, Gao J, Li X, Zhang X, et al. The associations of two novel inflammation indexes, SII and SIRI with the risks for cardiovascular diseases and all-cause mortality: a ten-year follow-up study in 85,154 individuals. J Inflamm Res. (2021) 14:131–40. doi: 10.2147/JIR.S283835

PubMed Abstract | Crossref Full Text | Google Scholar

40. Kadier K, Dilixiati D, Ainiwaer A, Liu X, Lu J, Liu P, et al. Analysis of the relationship between sleep-related disorder and systemic immune-inflammation index in the US population. BMC Psychiatry. (2023) 23:773. doi: 10.1186/s12888-023-05286-7

PubMed Abstract | Crossref Full Text | Google Scholar

41. Zhao Y, Shao W, Zhu Q, Zhang R, Sun T, Wang B, et al. Association between systemic immune-inflammation index and metabolic syndrome and its components: results from the National Health and Nutrition Examination Survey 2011-2016. J Transl Med. (2023) 21:691. doi: 10.1186/s12967-023-04491-y

PubMed Abstract | Crossref Full Text | Google Scholar

42. Yousefzadeh MJ, Flores RR, Zhu Y, Schmiechen ZC, Brooks RW, Trussoni CE, et al. An aged immune system drives senescence and ageing of solid organs. Nature. (2021) 594:100–5. doi: 10.1038/s41586-021-03547-7

PubMed Abstract | Crossref Full Text | Google Scholar

43. Bektas A, Schurman SH, Sen R, Ferrucci L. Aging, inflammation and the environment. Exp Gerontol. (2018) 105:10–8. doi: 10.1016/j.exger.2017.12.015

PubMed Abstract | Crossref Full Text | Google Scholar

44. Milan-Mattos JC, Anibal FF, Perseguini NM, Minatel V, Rehder-Santos P, Castro CA, et al. Effects of natural aging and gender on pro-inflammatory markers. Braz J Med Biol Res. (2019) 52:e8392. doi: 10.1590/1414-431x20198392

PubMed Abstract | Crossref Full Text | Google Scholar

45. Guan Y, Zhang C, Lyu G, Huang X, Zhang X, Zhuang T, et al. Senescence-activated enhancer landscape orchestrates the senescence-associated secretory phenotype in murine fibroblasts. Nucleic Acids Res. (2020) 48:10909–23. doi: 10.1093/nar/gkaa858

PubMed Abstract | Crossref Full Text | Google Scholar

46. Hu H, Huff CD, Yamamura Y, Wu X, Strom SS. The relationship between Native American Ancestry, body mass index and diabetes risk among Mexican-Americans. PLoS ONE. (2015) 10:e0141260. doi: 10.1371/journal.pone.0141260

PubMed Abstract | Crossref Full Text | Google Scholar

47. Bhupathiraju SN, Hu FB. Epidemiology of obesity and diabetes and their cardiovascular complications. Circ Res. (2016) 118:1723–35. doi: 10.1161/CIRCRESAHA.115.306825

PubMed Abstract | Crossref Full Text | Google Scholar

Keywords: Life's Essential 8, systemic immune-inflammation index, cardiovascular health, American-adult populations, cross-sectional study

Citation: Huang S-S, Yu X-S, Lin T, Xie Z-Y, Mao H-Y and Yang Z-X (2025) Associations between Life's Essential 8 scores and systemic immune-inflammation index among American-adult populations without cardiovascular disease. Front. Med. 12:1588835. doi: 10.3389/fmed.2025.1588835

Received: 17 March 2025; Accepted: 17 July 2025;
Published: 05 August 2025.

Edited by:

Ian James Martins, University of Western Australia, Australia

Reviewed by:

Maddalena Illario, University of Naples Federico II, Italy
Chen Jiageng, Tianjin Medical University, China

Copyright © 2025 Huang, Yu, Lin, Xie, Mao and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Hai-Yan Mao, bWFvbWFvMjAwMzY3OEAxNjMuY29t; Zhou-Xin Yang, eWFuZ3pob3V4aW5AaG90bWFpbC5jb20=

These authors share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.